Typically, an object shows slight variations in its color and appearance due to the variations of material composition or compound of different scene elements, and the capability of detecting such variations is one of the important factors for an image pickup system. In fields such as medical imaging, automatic inspection, and remote sensing, particularly, various imaging methods have been developed to detect anomalies such as, respectively, skin disease, food contamination, and deforestation using specific devices sensitive to corresponding spectral variations in surface reflectance.
However, recovery of surface reflection is impossible exclusively with conventional RGB cameras, since the appearance of a captured image depends on both the illumination spectrum and the spectral reflectance of the object in the scene. This is because, even if the illumination spectrum is known, an RGB camera provides only 3 measurements (Red, Green, and Blue) that are insufficient to recover the spectral reflectance. For this reason, conventional RGB-based computer-aided imaging devices and graphic techniques, implemented on the basis of the sum of simplified spectral weights of RGB rather than real colors, are limited in color expressions, whereby, it is known that the RGB-based imaging techniques inferior to the multispectral imaging techniques in color expression performance and applicability to variant illumination environments or media. Also, in a metameric environment in which some colors are not distinctive from each other, RGB imaging techniques are likely to ignore useful information.
In order to solve these problems, a wide variety of methods have been developed for estimating the spectral reflectance of a scene. For a static scene with fixed illumination, the spectral sensitivity of the camera can be varied over time such that, if the illumination spectrum is known, the multispectral reflectance of the scene can be determined. In the case of a dynamic scene, however, the spectral reflectance must be measured with high temporal resolution. Unfortunately, there exist no methods for capturing multispectral videos in real-time.
Instead of obtaining the spectral reflectance exclusively with a multispectral camera and a fixed illumination, the spectrum of the illumination can be modulated temporally, to provide a multispectral light source. Illumination spectrum modulation is advantageous since it is easier to create an illumination source with rapidly changing spectra than a camera with rapidly changing spectral sensitivity. Also, if there are M camera channels and N spectrally distinct illuminations, the number of effective channels is MN. This multiplicative effective dramatically increases the number of independent measurements with a minor increase in system complexity. Even in this case, in order to obtain a multispectral illumination to a dynamic scene, a large number of channels are required, resulting in an increase of M.
As described above, conventional multispectral imaging methods obtain multispectral illumination by changing the spectral sensitivity of the camera or the illumination spectrum. However, these conventional methods have drawbacks in that varying either the spectral sensitivity of the camera or the illumination typically comes at the cost of lowering the spatial resolution or the frame rate of the acquired data.
Accordingly, there has been a need for a novel multispectral imaging technique that is capable of obtaining multispectral image without a cost of lowering spatial resolution and frame rate.